Adapting myoelectric control in real-time using a virtual environment

Richard B. Woodward, Levi J Hargrove

Research output: Contribution to journalArticle

Abstract

Background: Pattern recognition technology allows for more intuitive control of myoelectric prostheses. However, the need to collect electromyographic data to initially train the pattern recognition system, and to re-train it during prosthesis use, adds complexity that can make using such a system difficult. Although experienced clinicians may be able to guide users to ensure successful data collection methods, they may not always be available when a user needs to (re)train their device. Methods: Here we present an engaging and interactive virtual reality environment for optimal training of a myoelectric controller. Using this tool, we evaluated the importance of training a classifier actively (i.e., moving the residual limb during data collection) compared to passively (i.e., maintaining the limb in a single, neutral orientation), and whether computational adaptation through serious gaming can improve performance. Results: We found that actively trained classifiers performed significantly better than passively trained classifiers for non-amputees (P < 0.05). Furthermore, collecting data passively with minimal instruction, paired with computational adaptation in a virtual environment, significantly improved real-time performance of myoelectric controllers. Conclusion: These results further support previous work which suggested active movements during data collection can improve pattern recognition systems. Furthermore, adaptation within a virtual guided serious game environment can improve real-time performance of myoelectric controllers.

Original languageEnglish (US)
Article number11
JournalJournal of neuroengineering and rehabilitation
Volume16
Issue number1
DOIs
StatePublished - Jan 16 2019

Fingerprint

Automated Pattern Recognition
Prostheses and Implants
Extremities
Technology
Equipment and Supplies

Keywords

  • Amputee
  • Electromyography
  • Myoelectric control
  • Pattern recognition
  • Real-time adaptation
  • Serious gaming
  • Upper-limb prostheses
  • Virtual guided training
  • Virtual rehabilitation

ASJC Scopus subject areas

  • Rehabilitation
  • Health Informatics

Cite this

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abstract = "Background: Pattern recognition technology allows for more intuitive control of myoelectric prostheses. However, the need to collect electromyographic data to initially train the pattern recognition system, and to re-train it during prosthesis use, adds complexity that can make using such a system difficult. Although experienced clinicians may be able to guide users to ensure successful data collection methods, they may not always be available when a user needs to (re)train their device. Methods: Here we present an engaging and interactive virtual reality environment for optimal training of a myoelectric controller. Using this tool, we evaluated the importance of training a classifier actively (i.e., moving the residual limb during data collection) compared to passively (i.e., maintaining the limb in a single, neutral orientation), and whether computational adaptation through serious gaming can improve performance. Results: We found that actively trained classifiers performed significantly better than passively trained classifiers for non-amputees (P < 0.05). Furthermore, collecting data passively with minimal instruction, paired with computational adaptation in a virtual environment, significantly improved real-time performance of myoelectric controllers. Conclusion: These results further support previous work which suggested active movements during data collection can improve pattern recognition systems. Furthermore, adaptation within a virtual guided serious game environment can improve real-time performance of myoelectric controllers.",
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Adapting myoelectric control in real-time using a virtual environment. / Woodward, Richard B.; Hargrove, Levi J.

In: Journal of neuroengineering and rehabilitation, Vol. 16, No. 1, 11, 16.01.2019.

Research output: Contribution to journalArticle

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AU - Hargrove, Levi J

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